Discourse Markers and Spoken English: Nonnative Use in the Turkish EFL Setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the production of discourse markers by non-native speakers of English and their occurrences in their spoken English by comparing them with those used in native speakers’ spoken discourse. Because discourse markers (DMs) are significant items in spoken discourse of native speakers, a study about the use of DMs by nonnative speakers is necessary and guiding. Thus, the study was based on two specific corpora. First, a research corpus was composed using the transcriptions of the course presentations of twenty non-native undergraduate students studying at an English Language Teaching (ELT) program in Turkey. To compare the data, transcripts of student presentations of native speakers were attained with the help of MICASE Corpus. The occurrences of the discourse markers in both corpora were determined with frequency analysis. The results indicated that non-native speakers of English use a limited number and less variety of discourse markers in their spoken English. The study therefore highlights the importance of the need for raising non-native speakers' awareness of using discourse markers in their spoken English, and recommends implications for English language teaching.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it